{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 用户消费行为分析\n",
    "    本专题主要对平台中33产品线的历史数据进行分析展示，目的是描绘用户画像，理解业务趋势与发现未知关键点，最终协助提升协助提升该产品运营效能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import missingno\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\" \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from datetime import datetime\n",
    "import matplotlib as mpl\n",
    "mpl.rcParams['font.sans-serif'] = ['FangSong']  # 指定默认字体\n",
    "mpl.rcParams['axes.unicode_minus'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sex(v_str):\n",
    "    \"\"\"\n",
    "    身份证区分 性别\n",
    "    \"\"\"\n",
    "    sex = int(v_str[16])\n",
    "    if sex%2:\n",
    "        sex=\"男\"\n",
    "    else:\n",
    "        sex=\"女\"\n",
    "    return sex\n",
    "\n",
    "def value_out_idx(df_dup,cols):\n",
    "    \"\"\"\n",
    "    获得到指定列的异常值\n",
    "    eg:\n",
    "    # df_dup_a = df_dup[~df_dup.index.isin(drop_idx_list)]\n",
    "    # drop_idx_list = value_out_idx(df_dup,['orderAmo','用户购买商品总数量','用户购买商品总金额','用户购买商品平均价格'])\n",
    "    \"\"\"\n",
    "    drop_idx_list = []\n",
    "    for col in cols:\n",
    "        ### 去除极端异常值\n",
    "        Percentile = np.percentile(df_dup[col],[0,25,50,75,100])\n",
    "        IQR = Percentile[3] - Percentile[1]\n",
    "        UpLimit = Percentile[3]+IQR*1.5\n",
    "        DownLimit = Percentile[1]-IQR*1.5\n",
    "\n",
    "        orderAmo_out_index = df_dup[(df_dup[col] > UpLimit) | (df_dup[col] < DownLimit)].index.tolist()\n",
    "        drop_idx_list.extend(orderAmo_out_index)\n",
    "    \n",
    "    \n",
    "    return list(set(drop_idx_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取数据&特征衍生"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(423383, 9)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f_P = r'E:\\工作文档\\工作任务\\20191116用户分析\\analysis.OrderCheckCol.csv'\n",
    "with open(f_P,encoding='utf-8') as f:\n",
    "    df = pd.read_csv(f)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15538"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drop_idx_list = value_out_idx(df_dup,['orderAmo','用户购买商品总数量','用户购买商品总金额','用户购买商品平均价格'])\n",
    "len(drop_idx_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "用户去重数:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "142647"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('用户去重数:')\n",
    "df.userMobile.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 原始数据\n",
    "df['orderTime'] = pd.to_datetime(df['orderTime'])\n",
    "df = df.sort_values(by=['userMobile','orderTime'],ascending=True)\n",
    "\n",
    "### 聚合数据\n",
    "df_dup = df.drop_duplicates(subset=['userMobile'],keep='last')\n",
    "df1 = df.groupby('userMobile')['article.0.artCount'].sum().reset_index().rename(columns={'article.0.artCount':'用户购买商品总数量'})\n",
    "df2 = df.groupby('userMobile')['article.0.artPrice'].sum().reset_index().rename(columns={'article.0.artPrice':'用户购买商品总金额'})\n",
    "df3 =df.groupby('userMobile').size().reset_index().rename(columns={0:'用户下单次数'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dup.drop(['article.0.artCount','article.0.artName','article.0.artPrice'],axis=1,inplace=True)\n",
    "df_dup = pd.merge(df_dup,df1,on='userMobile',how='left')\n",
    "df_dup = pd.merge(df_dup,df2,on='userMobile',how='left')\n",
    "df_dup = pd.merge(df_dup,df3,on='userMobile',how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(142647,)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(142647,)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dup['用户购买商品总金额'].map(lambda x :type(x)).shape\n",
    "df_dup['用户购买商品总数量'].map(lambda x :type(x)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dup['用户购买商品平均价格'] = df_dup['用户购买商品总金额'] / df_dup['用户购买商品总数量']\n",
    "df_dup['用户性别'] = df_dup['idNum'].map(get_sex)\n",
    "df_dup['用户最后一次购买时间距现在天数'] = df_dup['orderTime'].map(lambda x: (datetime.today() - x).days)\n",
    "df_dup['month'] = df_dup['orderTime'].astype('datetime64[M]')\n",
    "df['month'] = df['orderTime'].astype('datetime64[M]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userMobile</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>orderCode</th>\n",
       "      <th>orderAmo</th>\n",
       "      <th>instalmentRate</th>\n",
       "      <th>idNum</th>\n",
       "      <th>month</th>\n",
       "      <th>用户购买商品总数量</th>\n",
       "      <th>用户购买商品总金额</th>\n",
       "      <th>用户下单次数</th>\n",
       "      <th>用户购买商品平均价格</th>\n",
       "      <th>用户性别</th>\n",
       "      <th>用户最后一次购买时间距现在天数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13001031231</td>\n",
       "      <td>2016-09-18</td>\n",
       "      <td>311609181545532384917</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>362401198510210015</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>男</td>\n",
       "      <td>1157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13001101801</td>\n",
       "      <td>2017-02-15</td>\n",
       "      <td>3117021500120000010</td>\n",
       "      <td>5199.0</td>\n",
       "      <td>0.144</td>\n",
       "      <td>220722199212264218</td>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>18</td>\n",
       "      <td>82581.0</td>\n",
       "      <td>18</td>\n",
       "      <td>4587.833333</td>\n",
       "      <td>男</td>\n",
       "      <td>1007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13001140007</td>\n",
       "      <td>2016-07-25</td>\n",
       "      <td>311607251723422383739</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>0.120</td>\n",
       "      <td>14032119920418421X</td>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4485.000000</td>\n",
       "      <td>男</td>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13001352283</td>\n",
       "      <td>2016-11-17</td>\n",
       "      <td>311611170231442380142</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.144</td>\n",
       "      <td>372527198010293716</td>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>8</td>\n",
       "      <td>31677.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3959.625000</td>\n",
       "      <td>男</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13001454260</td>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>3116082719125323816573</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>130582199103132033</td>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>男</td>\n",
       "      <td>1179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userMobile  orderTime               orderCode  orderAmo  instalmentRate  \\\n",
       "0  13001031231 2016-09-18   311609181545532384917      69.0           0.030   \n",
       "1  13001101801 2017-02-15     3117021500120000010    5199.0           0.144   \n",
       "2  13001140007 2016-07-25   311607251723422383739    4485.0           0.120   \n",
       "3  13001352283 2016-11-17   311611170231442380142    2800.0           0.144   \n",
       "4  13001454260 2016-08-27  3116082719125323816573      69.0           0.030   \n",
       "\n",
       "                idNum      month  用户购买商品总数量  用户购买商品总金额  用户下单次数   用户购买商品平均价格  \\\n",
       "0  362401198510210015 2016-09-01          1       69.0       1    69.000000   \n",
       "1  220722199212264218 2017-02-01         18    82581.0      18  4587.833333   \n",
       "2  14032119920418421X 2016-07-01          1     4485.0       1  4485.000000   \n",
       "3  372527198010293716 2016-11-01          8    31677.0       8  3959.625000   \n",
       "4  130582199103132033 2016-08-01          1       69.0       1    69.000000   \n",
       "\n",
       "  用户性别  用户最后一次购买时间距现在天数  \n",
       "0    男             1157  \n",
       "1    男             1007  \n",
       "2    男             1212  \n",
       "3    男             1097  \n",
       "4    男             1179  "
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 通过对用户ID分组,并进行求和,再进行描述性统计\n",
    "df_dup.shape[0] == df['userMobile'].nunique()\n",
    "\n",
    "df_dup.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 订单与用户分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ba4769feb8>]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '每月消费总金额变化趋势')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ba44826780>]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '每月订单数变化趋势')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ba5b2acd30>]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '每月消费产品购买量')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ba4bb97f28>]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '每月用户数变化趋势')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#按月分组\n",
    "grouped_month = df_dup.groupby('month')\n",
    "#制定可视化图分析不同维度用户消费趋势\n",
    "fig = plt.figure(figsize = (8,12))\n",
    "ax1 = fig.add_subplot(4,1,1)\n",
    "ax2 = fig.add_subplot(4,1,2)\n",
    "ax3 = fig.add_subplot(4,1,3)\n",
    "ax4 = fig.add_subplot(4,1,4)\n",
    "\n",
    "# 按月金额数(图1)\n",
    "ax1.plot(grouped_month.orderAmo.sum(),c='blue')\n",
    "ax1.set_title('每月消费总金额变化趋势')\n",
    "\n",
    "# 按月的订单数(图2)\n",
    "ax2.plot(grouped_month.userMobile.count(),c='blue')\n",
    "ax2.set_title('每月订单数变化趋势')\n",
    "\n",
    "# 按月产品数(图3)\n",
    "ax3.plot(grouped_month.用户购买商品总数量.sum(),c='blue')\n",
    "ax3.set_title('每月消费产品购买量')\n",
    "\n",
    "# 每月用户人数  这里需要去重，有些用户重复购买(图4)\n",
    "ax4.plot(df_dup.groupby('month').size(),c='blue')\n",
    "ax4.set_title('每月用户数变化趋势')\n",
    "# 另一种去重方式：df.groupby(['month','user_id']).count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#每月用户消费的平均金额(图5)\n",
    "# ((grouped_month.orderAmo.sum())/(grouped_month.userMobile.apply(lambda x: len(x.drop_duplicates())))).plot()\n",
    "sns.lineplot(x='month',y='用户购买商品总金额',data=df_dup,estimator='mean')\n",
    "plt.title('每月用户消费的平均金额');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userMobile</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>orderCode</th>\n",
       "      <th>orderAmo</th>\n",
       "      <th>instalmentRate</th>\n",
       "      <th>idNum</th>\n",
       "      <th>month</th>\n",
       "      <th>用户购买商品总数量</th>\n",
       "      <th>用户购买商品总金额</th>\n",
       "      <th>用户下单次数</th>\n",
       "      <th>用户购买商品平均价格</th>\n",
       "      <th>用户性别</th>\n",
       "      <th>用户最后一次购买时间距现在天数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13001031231</td>\n",
       "      <td>2016-09-18</td>\n",
       "      <td>311609181545532384917</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>362401198510210015</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>男</td>\n",
       "      <td>1157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13001101801</td>\n",
       "      <td>2017-02-15</td>\n",
       "      <td>3117021500120000010</td>\n",
       "      <td>5199.0</td>\n",
       "      <td>0.144</td>\n",
       "      <td>220722199212264218</td>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>18</td>\n",
       "      <td>82581.0</td>\n",
       "      <td>18</td>\n",
       "      <td>4587.833333</td>\n",
       "      <td>男</td>\n",
       "      <td>1007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13001140007</td>\n",
       "      <td>2016-07-25</td>\n",
       "      <td>311607251723422383739</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>0.120</td>\n",
       "      <td>14032119920418421X</td>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4485.000000</td>\n",
       "      <td>男</td>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13001352283</td>\n",
       "      <td>2016-11-17</td>\n",
       "      <td>311611170231442380142</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.144</td>\n",
       "      <td>372527198010293716</td>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>8</td>\n",
       "      <td>31677.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3959.625000</td>\n",
       "      <td>男</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13001454260</td>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>3116082719125323816573</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>130582199103132033</td>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>男</td>\n",
       "      <td>1179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userMobile  orderTime               orderCode  orderAmo  instalmentRate  \\\n",
       "0  13001031231 2016-09-18   311609181545532384917      69.0           0.030   \n",
       "1  13001101801 2017-02-15     3117021500120000010    5199.0           0.144   \n",
       "2  13001140007 2016-07-25   311607251723422383739    4485.0           0.120   \n",
       "3  13001352283 2016-11-17   311611170231442380142    2800.0           0.144   \n",
       "4  13001454260 2016-08-27  3116082719125323816573      69.0           0.030   \n",
       "\n",
       "                idNum      month  用户购买商品总数量  用户购买商品总金额  用户下单次数   用户购买商品平均价格  \\\n",
       "0  362401198510210015 2016-09-01          1       69.0       1    69.000000   \n",
       "1  220722199212264218 2017-02-01         18    82581.0      18  4587.833333   \n",
       "2  14032119920418421X 2016-07-01          1     4485.0       1  4485.000000   \n",
       "3  372527198010293716 2016-11-01          8    31677.0       8  3959.625000   \n",
       "4  130582199103132033 2016-08-01          1       69.0       1    69.000000   \n",
       "\n",
       "  用户性别  用户最后一次购买时间距现在天数  \n",
       "0    男             1157  \n",
       "1    男             1007  \n",
       "2    男             1212  \n",
       "3    男             1097  \n",
       "4    男             1179  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dup.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 透视表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>instalmentRate</th>\n",
       "      <th>orderAmo</th>\n",
       "      <th>用户最后一次购买时间距现在天数</th>\n",
       "      <th>用户购买商品总数量</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-06-01</td>\n",
       "      <td>0.059414</td>\n",
       "      <td>4001.054688</td>\n",
       "      <td>1237.007812</td>\n",
       "      <td>1.203125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>0.066170</td>\n",
       "      <td>3605.719414</td>\n",
       "      <td>1225.173051</td>\n",
       "      <td>1.915191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>0.086356</td>\n",
       "      <td>2135.535617</td>\n",
       "      <td>1185.095292</td>\n",
       "      <td>2.120850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>0.081350</td>\n",
       "      <td>1966.967027</td>\n",
       "      <td>1158.703895</td>\n",
       "      <td>2.615437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>0.128619</td>\n",
       "      <td>2709.351907</td>\n",
       "      <td>1130.054260</td>\n",
       "      <td>4.057044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>0.135881</td>\n",
       "      <td>2847.122352</td>\n",
       "      <td>1100.615500</td>\n",
       "      <td>5.326727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>0.134831</td>\n",
       "      <td>2479.987711</td>\n",
       "      <td>1071.693548</td>\n",
       "      <td>6.185484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>0.137833</td>\n",
       "      <td>2205.991649</td>\n",
       "      <td>1032.781585</td>\n",
       "      <td>5.276231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>0.137244</td>\n",
       "      <td>2221.634546</td>\n",
       "      <td>1006.842939</td>\n",
       "      <td>5.703170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>0.135541</td>\n",
       "      <td>2277.719752</td>\n",
       "      <td>976.654081</td>\n",
       "      <td>8.116323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       month  instalmentRate     orderAmo  用户最后一次购买时间距现在天数  用户购买商品总数量\n",
       "0 2016-06-01        0.059414  4001.054688      1237.007812   1.203125\n",
       "1 2016-07-01        0.066170  3605.719414      1225.173051   1.915191\n",
       "2 2016-08-01        0.086356  2135.535617      1185.095292   2.120850\n",
       "3 2016-09-01        0.081350  1966.967027      1158.703895   2.615437\n",
       "4 2016-10-01        0.128619  2709.351907      1130.054260   4.057044\n",
       "5 2016-11-01        0.135881  2847.122352      1100.615500   5.326727\n",
       "6 2016-12-01        0.134831  2479.987711      1071.693548   6.185484\n",
       "7 2017-01-01        0.137833  2205.991649      1032.781585   5.276231\n",
       "8 2017-02-01        0.137244  2221.634546      1006.842939   5.703170\n",
       "9 2017-03-01        0.135541  2277.719752       976.654081   8.116323"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_group1 = df_dup.pivot_table(index='month',values=['orderAmo','instalmentRate','用户购买商品总数量','用户最后一次购买时间距现在天数']\n",
    "                                  ,aggfunc={\n",
    "                                      'orderAmo':'mean',\n",
    "                                      'instalmentRate':'mean',\n",
    "                                      '用户购买商品总数量':'mean',\n",
    "                                      '用户最后一次购买时间距现在天数':'mean'\n",
    "                                      }\n",
    "                                  )\n",
    "temp_group1.reset_index(inplace=True)\n",
    "temp_group1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba3854b208>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='month',y='instalmentRate',data=temp_group1) # 均值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析： 可见利率逐步提升，到达0.13后稳定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba36d26b00>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='month',y='orderAmo',data=temp_group1) # 均值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：数据在1606之后出现急速下滑，在1609后有短暂冲量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba36db4da0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='month',y='用户购买商品总数量',data=temp_group1) # 均值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：用户购买商品数量成上升趋势"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 .用户个体消费分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 用户消费金额，消费次数描述统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>orderAmo</th>\n",
       "      <th>instalmentRate</th>\n",
       "      <th>article.0.artCount</th>\n",
       "      <th>article.0.artPrice</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userMobile</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13001031231</th>\n",
       "      <td>69.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001101801</th>\n",
       "      <td>82581.0</td>\n",
       "      <td>2.400</td>\n",
       "      <td>18</td>\n",
       "      <td>82581.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001140007</th>\n",
       "      <td>4485.0</td>\n",
       "      <td>0.120</td>\n",
       "      <td>1</td>\n",
       "      <td>4485.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001352283</th>\n",
       "      <td>31677.0</td>\n",
       "      <td>0.912</td>\n",
       "      <td>8</td>\n",
       "      <td>31677.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001454260</th>\n",
       "      <td>69.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             orderAmo  instalmentRate  article.0.artCount  article.0.artPrice\n",
       "userMobile                                                                   \n",
       "13001031231      69.0           0.030                   1                69.0\n",
       "13001101801   82581.0           2.400                  18             82581.0\n",
       "13001140007    4485.0           0.120                   1              4485.0\n",
       "13001352283   31677.0           0.912                   8             31677.0\n",
       "13001454260      69.0           0.030                   1                69.0"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
       "             orderTime  orderCode  orderAmo  instalmentRate  idNum  \\\n",
       "userMobile                                                           \n",
       "13001031231          1          1         1               1      1   \n",
       "13001101801         18         18        18              18     18   \n",
       "13001140007          1          1         1               1      1   \n",
       "13001352283          8          8         8               8      8   \n",
       "13001454260          1          1         1               1      1   \n",
       "\n",
       "             article.0.artCount  article.0.artName  article.0.artPrice  month  \n",
       "userMobile                                                                     \n",
       "13001031231                   1                  1                   1      1  \n",
       "13001101801                  18                 18                  18     18  \n",
       "13001140007                   1                  1                   1      1  \n",
       "13001352283                   8                  8                   8      8  \n",
       "13001454260                   1                  1                   1      1  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userMobile</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>orderCode</th>\n",
       "      <th>orderAmo</th>\n",
       "      <th>instalmentRate</th>\n",
       "      <th>idNum</th>\n",
       "      <th>month</th>\n",
       "      <th>用户购买商品总数量</th>\n",
       "      <th>用户购买商品总金额</th>\n",
       "      <th>用户下单次数</th>\n",
       "      <th>用户购买商品平均价格</th>\n",
       "      <th>用户性别</th>\n",
       "      <th>用户最后一次购买时间距现在天数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13001031231</td>\n",
       "      <td>2016-09-18</td>\n",
       "      <td>311609181545532384917</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>362401198510210015</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>男</td>\n",
       "      <td>1157</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13001101801</td>\n",
       "      <td>2017-02-15</td>\n",
       "      <td>3117021500120000010</td>\n",
       "      <td>5199.0</td>\n",
       "      <td>0.144</td>\n",
       "      <td>220722199212264218</td>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>18</td>\n",
       "      <td>82581.0</td>\n",
       "      <td>18</td>\n",
       "      <td>4587.833333</td>\n",
       "      <td>男</td>\n",
       "      <td>1007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13001140007</td>\n",
       "      <td>2016-07-25</td>\n",
       "      <td>311607251723422383739</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>0.120</td>\n",
       "      <td>14032119920418421X</td>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4485.000000</td>\n",
       "      <td>男</td>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13001352283</td>\n",
       "      <td>2016-11-17</td>\n",
       "      <td>311611170231442380142</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.144</td>\n",
       "      <td>372527198010293716</td>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>8</td>\n",
       "      <td>31677.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3959.625000</td>\n",
       "      <td>男</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13001454260</td>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>3116082719125323816573</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>130582199103132033</td>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>男</td>\n",
       "      <td>1179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userMobile  orderTime               orderCode  orderAmo  instalmentRate  \\\n",
       "0  13001031231 2016-09-18   311609181545532384917      69.0           0.030   \n",
       "1  13001101801 2017-02-15     3117021500120000010    5199.0           0.144   \n",
       "2  13001140007 2016-07-25   311607251723422383739    4485.0           0.120   \n",
       "3  13001352283 2016-11-17   311611170231442380142    2800.0           0.144   \n",
       "4  13001454260 2016-08-27  3116082719125323816573      69.0           0.030   \n",
       "\n",
       "                idNum      month  用户购买商品总数量  用户购买商品总金额  用户下单次数   用户购买商品平均价格  \\\n",
       "0  362401198510210015 2016-09-01          1       69.0       1    69.000000   \n",
       "1  220722199212264218 2017-02-01         18    82581.0      18  4587.833333   \n",
       "2  14032119920418421X 2016-07-01          1     4485.0       1  4485.000000   \n",
       "3  372527198010293716 2016-11-01          8    31677.0       8  3959.625000   \n",
       "4  130582199103132033 2016-08-01          1       69.0       1    69.000000   \n",
       "\n",
       "  用户性别  用户最后一次购买时间距现在天数  \n",
       "0    男             1157  \n",
       "1    男             1007  \n",
       "2    男             1212  \n",
       "3    男             1097  \n",
       "4    男             1179  "
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_group2 = df.groupby('userMobile')\n",
    "temp_group2_sum = temp_group2.sum()\n",
    "temp_group2_cnt = temp_group2.count()\n",
    "temp_group2_sum.head()\n",
    "temp_group2_cnt.head()\n",
    "df_dup.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba53ff3550>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 每笔订单消费金额与消费商品数的直方图 \n",
    "# y轴为消费金额平均值\n",
    "sns.barplot(x='用户下单次数',y='orderAmo',data=df_dup_a,estimator=np.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba475e5fd0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='用户下单次数',y='orderAmo',data=df_dup_a,estimator=np.mean) ## 平均"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 用户消费金额的分布图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba47519390>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OVFR3b6FYa8eV5rGivJrmZo9zq0RE4ksh0IHtVXU9DoGppbnsr29k856DcW6ViEh86ZpAO9yd8n21nDm5uEfPn1qaB8A7ZVWMK8yKZ9PkCKEptTJYaCTQjr0HG6hraGbEkNhXC7d2VHEOqUkRVui6gIgMcgqBdpTtrQWgdGjPQiA1OcLRw3N4RyEgIoNcTKeDzOxmYC9Q6e4PtvN4EvADYCewzN0XmFkOcEvwvCfdfYmZlQLXAXXAfe6+1cxOBUqAMcBCd18Uh371Stm+IAR6OBKA6Cmhp5aW4+4xb0UtItLfuhwJmNkMoNbd7wHmmFlqO9XOBxa7+53AZUHZlcCjwK3ANUHZjcCdwB3ADUHZFe7+e+BHwFU97Ug8xSUERuRRXdfI1j218WqWiEjcxTISmAu8GByvA2YBL7dT57vB8UEzGwmcCdzj7k1mVmhmEWCSu1cAmNmEoP6GVq9T14M+xF35vloyUpIYkhnbHcXau8h3XKuLw6MLMuPaPhGReIklBEYAu4LjPcDwGOtkuHvLfRb3AwVA61FEGoC7fz/4+hxgfnsNMLOrCEYJo0f3/QyK8n21lA7N6NVpnMklOWSmJvHqht2cO629f7LE1NHKXBFJTN29MGxAVyugWup4F2XvP2iWB4xz96fae9zd57n7THefWVRU1M0md1/ZvtoezwxqkZoc4fSJhTz37k7cw7FozN3ZUV3HqxsqWbRxz3un1URk8IplJFAOFAKrgXxgeSd1yoI624E6M0sKRgNZREcIDa2eUwdg0V+3Lwd+0sM+xF35vlqOHZHX69f5yJRinl65g1UVNUwZnhuHlg1OhxqbeX1jJQvX7aa6rvG98ieWlDG1NJf/uvgEjhqWM4AtHFgaPclgFksIzCd6fn8hMBH4sZnlu/ueNnVOBR4jehqozMyeB2aY2VtEZxU1m9laMysGDgHrg+deCDwSPP5Rd38mTn3rkbqGJnbvP0TpkJ6tFm7tjGCx2XOrdh6RIdDszhub9vDcuzupqW9kYlE2Z03JY3xRNo3NzWSnJfPTlzZw4U9eYd5nZ3LKhIKBbnKHXttQyU9eWEdJXjpTS/MYX5gdqvtBSHh1GQLu/qaZnWtm1wIvED13fzzwrVbVngBuM7PxwENB2TzgZuBc4N6g7Hais4IagbuCsn8BPh1cOK4BBjQEyoNTGL09HQRQnJvOtJF5PLdqJ189Y2KvX28w2bLnIE8uKaO8qo6xBZlcMmv0YaujL509mnOmlvCFX77B537xOvdeMp25xw2+6yPPvruDrzz0FmnJEXbU1PPGpr1MHpbDZ08Z0yffb++BQ6QmR8hK04J9GXgx/RS6+y1tih5r83gTcFObshrg+jZlZbw/NbSl7PRYG9sf4jE9tLUzjy7mnmfXsufAIfKz2ptdm1ia3fnbml08vXIHOenJXHLSKI4rzevwIvrIoZn87h9P5YpfLeLrjy4hPyuV2eMHz4hg/vLt/PPDb3PMiFw+MW0EKckRXl63m2dW7uCV9ZVcfnJ8gqCuoYklW/fx1pa9bNtbS1LEOHZELqeOL2B0gbYWkYGjFcNtxHMkANEQcIcXVu+My+sNpJq6Bh58bTMLVu5gamke/3LWJKaNHNLlLKq8zBR+/vmTGJWfwZUPLGbNjpp+anHnyvbVcsNjy5hamsdDX55NZloyKUkR5kwqYkpJDguWV/RqS/Cqgw0s2bqPv7yzndvnr+LJpeU0NjnnHFvCrHH5rNlRw09f2sC2vdpoUAaOxqNtlO2tJWL0eAfRtqaOyKMoJ41nV+3kwhkj4/KaA6G+sem9D/Dzpg3nlPEF3ZpCOzQrlV9dMYsLf/IKn/v5Ih6+cjbji7L7sMWda252bnp8GU3u3HvJdHLS318TYmZcOGMk9z63lq8/+jZ/+trpZKZ2/l9lf30jP/rrGp5ftZO9BxvYd/DQexfJIxZdQX7ahEJG5b+/ZuSso4fxo2fX8Ie3y/jKnCPrdKEkDoVAG2X76hiWm05KUnwGSZGIcc6xJfzmja3sqK7r9o3rB1LLrJZmdx59YyvLy6r49IkjmT56aI9eb1R+Jg98cRaX/+x1Lv7pazz45VkcXdL7C+ZNzc62vQcxjNyMZLLSkol0EVAPvb6Zl9ft5tZPTm13MV9WWjKfPnEUv3xlI9976l2+f+FxHX7vp1dU8OqGShqbnVnj8pk+Oou8jBRGDs1gbEEW63cdIC/j8IWHGalJfHzaCB5etIWX1+3us2sQIp1RCLRRtu9g3K4HtLjyQ+N5eNEW5r20gW+fd0xcX7s/zA9Oi8ydWtLjAGgxZXguv7n6FC7/2et85qevcfunpnH2scN6tDDvrS17efj1LTy3aid7Dhx6rzwvI4UzJhdz4pj22/rahkpu/fO7fHhSEZfO6njx4cTibK7+8ATue3E9fzepkHOmfvCidu2hJh5etJn1uw4wY/RQ7rjoOCYWHz4VtrMpolNL8zhmeC7PvruDTbsPMFZbj0s/Uwi0Ub6vjhNGDYnra44uyOSCE0p56PXN/NOcCRR2877FA2nNjhpeXrebk8fn86Gj4rNQb2JxNo/94ylc+cBi/vHBN/nQUYVMGzmEEXnph4VB2z32m5qdF9fsZN5LG3htwx5y0pM58+hi0lOSSIkY+2obWLp1H08sKeNva3eRl5nCeccNJxJM93x9QyVX/PINRg3N5O6Lj+8yfK776CReWb+bm373DuOLspkUrHfYtPsAv397G3sPNHDRjJHMGDO03QCIxSeOH8EPn1nNT1/a0OGIQ6SvKARaaW52tlfV9sk2D185YwK/f3sbP395Izedc3TcX78vHKxv5HdvbaM4J425U7v/b9LZzd9H5Wfy1NdO59evbebup9fwt7W7yc9KZUpJDiOGZDAsN53M1CQq99ezv76RVRU1LNm6jz+8VUZFdR3DctP41rlT+IdZo8lKS/7A9zplfAGrKmp4ZuUOrnnkbX7ywnpOn1jA7v2HWLCigtKhGTx85ckxhXFqcoR7LpnOJ378Mmf/6CU+cvQwkiPG/BUV5KYn88XTx/X6xkG5GSkcV5rHk0vK+Pdzp5CtqaPSj/TT1squ/fU0NHncZga1NqEom/OmjeCBVzbxxdPGUZQzuEcD7s4TS8s5WN/E508ZG7drJK0lJ0W44rRxXHBCKbf8aSUrtlfx2sY9NLW6N/MdC1a/d2zAUcOy+c7HZ/CRKcNITW6/TWbGlOG5TC7JITstmR/9dQ0PvLqZopw0Zo/L5/aLpnXr339cYRbPXT+HX7+6iV+/tpn6xmbOmlLM6ROLOmxDd80am89bW/bx5JJy3WFM+pVCoJVtLTeTicNq4fZ+C55QmMUz7lz968U8fOXJpKck9fr79JUFK6LXAT52zLA+CcXWhmalctK4fE4al09Ts1O5v54dNfXUNzTR0OykRIxhuekU56aRlpwU84KziBkXTC/l/BNGAPRqQ8CinDSu+9hkvnLGRNzhD2+X9fi12jMqP5OjS3J4eNFmhYD0K60TaGX9zv0AjC/sm6mLxbnp3H3xCby1ZR83Pr5s0G4sV1PXwHeeXMHwvPS4XQeIVVLEKM5N57jSPGaOzeeU8QXMHJvPqPxM0pJ7FppmFrcb+6SnJJGRGv/wNjMunT2a5WXVLNu2L+6vL9IRhUArqypqSE+JMDq/7/b///vjhnPD2ZN5cmk53/zDcg4eauz6Sf3sh0+vYWdNPRecUKr9c/rRBdNLyUhJ0oZz0q90OqiVNTtqmDQs572ZJH3lK3MmUF3bwE9f2sDL63bxnxccx4ePKhwUt6FcunUf97+6ic+ePOYDC5sGkyP1QzI3PYXzpg3nT0vL+Y+PH9PlAjWReNBIoJXVQQj0NTPj3/5+Cr+9+hSSIxE+/4tFnP2jl/jlwo1U7q/v8+/fkcamZr75h3coyk7jG2dPHrB2hNlFJ47kwKEm5i+vGOimSEgoBAJ7DhxiV009R5f03773s8bl85evf4gLp5dy8FAT3/3TSk669a+cdfeL/OaNLew7eKjrF4mj+1/dzIryam7+xLHkpsd2a02Jr5PG5jM6P5PfvbVtoJsiIaHxZmB1RXRTs74eCbR3KmPm2Hxmjs2noqqOZdv2saysipt+9w7femI5HzqqiE/NGMlZxxT3+MJoLMr31fLDp1dzxuQi5k4t6bPvI52LRIwLZ5Ryz7NrKdtXG/fV6yJtKQQCLTtbTu7HkUBbJXnplOSV8NFjhjFt5BCeWlbOk0vL+erDbzE0M4VPTh/JxSeNjMt+O625O1/61Rs0NDUzc0w+jyzaGtfXl+751IyR/Oiva/nDW9v45zOPGujmyBFOIRBYVVHDkMwUigfBIi4z452yKsYUZPHVMyaybud+Fm/ey/2vbOIXCzdy/KghfO7kMZx3/HDSkjueTRLrfPNHFm3l3Yoa5k4tYWg/3PPgSL2wGy+j8jOZPS6fx9/cxlfPmDgoJgzIkUshEGiZGTTY/sNFzJg0LIdJw3I4UN9IUsR46PXNXP/YUr7/l3f58ofGk56c1OOVq6sravjun1ZwVHE2p00sjHPrpacuOnEkNzy+jFc3VHLqBL0v0nd0YZjo6ZA1FTX9elG4J7LSkklPSeKLp43jitPGMjQzlR/8ZRV3P7OaxZv20NzNxWe1h5r42iNvkZOewkUnjuxy+2XpPx8/fgQFWanMe2nDQDdFjnAaCQDlVXXU1Df2y/TQeDAzjirO4ajiHDbuPsCCFRX8/u0y3ty8l/Onl1ISwz0Lag9FbxKzdud+7r9i1ntbZoRBIpyOSk9J4ounj+POBatZWV7NMSPiex1IpIVGAsCaioG/KNxT4wqzuPrD4/nUjJHs2l/Pj59by4IVFRxqbO7wObWHmvjyA2+wcP1u7rzoeD48qX+3hpDYXH7yGLLTkrnvxfUD3RQ5gmkkQHSRGPT99NC+YmacOGYoR5fk8JflFby4ZhfLtu0jNyOZuVOHv7fXjbvz4ppd3D5/Nasqqvnhp49P6FteDjbxHmHkZaRw6ezR/OxvG/jGxya3ewc0kd5SCADvlFUxPC+93VsAJpKstGQuOnEkM0YP4cml5Vz326V8548rOGVCAQ5sr6pleVk1o/IzmPfZmXz0mGED3WTpwpdOH8evFm7ijgWr+H//MH3QTVyQxBf6EKhvbOKl1bv65EYyA2V8UTbXfOQoJhZn89s3trJ02z5Sk5PITE3ie+cfy2dOGh23ffClbw3LTeeaj0zkrqfXMH30UL50+riBbpIcYUIfAq+sq6SmvpGzj7BVshEzTh5fwMnjCwa6KdJLX5kzkXfKqrjtz+8yZXiOpoxKXIU+BP6yfDs5acmcOiEcH5aJMDMmno6E/kYixg8vPoEL/v9CrnrgTb50+jhy0pPb3WVUN6SR7gp1CDQ2NfPMyh18ZErf7ssj0lvZacn88gsn8b2nVnLPs2tJTYpQkJ1KWnIS6SkR0lOip/ve3rKXMQVZDM1Mee/6gYJBOhPqEFi0cQ97DzZwzhF2KqjFkfBbsLxvVH4m8z43k1UV1Xznjyuoqm2grqGZqtoGdlTXcaC+iVeaKgEYMSSdv5tUzLFaXyBdCHUIzF9RQXpKRPPkJaEcXZLL+SeUHlbe7M6O6jo27j7AaxsqeWTRFoqy0xhfmMWp2hJEOhDaEDjU2MyCFRX83aQi3cFJBrVYR3QRM4bnZTA8L4OTxxeworyaBSsquPRnr/PJ6aX8+7lTKMwe+A0SZXAJ7aff7fNXsaO6nktm6XypDA7xPH0XMeO40jyOLslh9/567ntxPc++u4N/nTuFS04a1ee3UJXEEcoQeGblDn7+8ka+cOpYzphcPNDNkUEs0a+rpCRFuP5jkzn/hFK+9cQ7fPMP7/DoG1u45syj+MiUYi0+k9hCwMxuBvYCle7+YDuPJwE/AHYCy9x9gZnlALcEz3vS3ZeYWSlwHVAH3OfuW81sGnAJ0X2MbnP36jj0q0PLy6r4xmNLOa40j3/7+6P78luJDBoTi7N55MqTeWJJGXc/s4YvP7CYKcNzuXB6KWcfW6ItKUKsyxAwsxlArbvfY2Y/M7Pfunvbm9+eDyx299+Y2QPAAuBK4FFgMfA/wBeBG4HvA7XA94BrgG8EdY8CrgbujEvPWnF3Fm3cw09eXM8Lq3cxNM5yn64AAAdfSURBVDOFH186XdNCJVTMjE9OH8l500bwxyXl/HLhRm7987vc+ud3GZGXzlHDchhXmEV+Vip5GSmkJUdISYqQnGSkJkVIToqQkmSkJEWCP+8fv1/HSI5EH0tOipAcMZIjRlLEQj3qcHdadnr31mXvHUPLV613hH//OdGDjJSkuP87xjISmAu8GByvA2YBL7dT57vB8UEzGwmcCdzj7k1mVmhmEWCSu1cAmNmEoCzf3evNbAXRgOiDEIB//f07VNc2cMPZk7l89hjyMhN7nyCRWHV0Suuy2WPYc+AQ726vpmxfLWt21PDm5r3sr2/sk3akJBkRM3ryGWb07IOvp5+XrT9823540+YDu/WHeku9bt7aI2arvncO6Snx/eU1lhAYAewKjvcA7W2y016dDHdvCsr2AwVA63sXpgVlNQDu7mbW7l21zewq4KqW1zKz1TG0u11vAf/c0ydDIbC7508ftNSvxKJ+JZa49Svj9l49fUx7hd29MGy0CsQu6ngXZTFz93nAvJ48N57MbLG7zxzodsSb+pVY1K/EMtj7FctWkuVEkwwgH9geY5264IIxQBbREUJDq+fUAZVALkBwauhgdxovIiK9E0sIzAdODY4nAsvMLL+TOhnuXgY8D8wIgqDS3ZuBtWZWbGZDgPVBWaWZpQBTgZd62R8REemGLk8HufubZnaumV0LvACcAxwPfKtVtSeA28xsPPBQUDYPuBk4F7g3KLsduAFoBO4Kyu4K6qUA/9nzrvSLAT8l1UfUr8SifiWWQd0v8766jC0iIoOebi8lIhJiCgERkRAL5d5BPdHV1hmDVXBh/vNE2z7V3b/Xti+xbvsxMD3onJkdA3zqSOqXmV1CdDr1h4muqu/RliwD0/r2mVkecCnRmYNF7v4/if5+mdln3P03wfHN9KAvg+J9iy5n1p/O/gAzgJuC458BqQPdpm60fS5wYXB8HdEPlg/0BbgQ+ExQ9kCrurOBJOAXA92PTvr3TaITCw57jxKxX8BI4Org+PPAp2LpA3APUALkAfcOdD/a6dfXgLzg+FOJ/nMIfBz4S3Dc45+9wfC+6XRQbOYCC4Pjlq0zEsVWorOxWpzB4X1p3b/W234s9uiq75ZtPwaVYF+rxcGX7b1HidivTxJd2I673090Nl4sfZjk7hXuXgVMGIB2d6WG6PsB0Z0CEvrn0N3/BOwIvuzNz96Av286HRSbWLbOGJTcfTmwPPhyPNHV2237Euu2Hy11BotJwGtE16i014dE7NdYINXMPkR0mX9Pt2QZbH4N/M7MPkZ0PVAxR8b7Bb372Rvw921QpGqCiWXrjEHHzD4D3N22mMP70tm2H4OGmZ0G/K2jh0nQfgE5wCp3vxtYBqS3eizuW7L0oynA74E/A1fwwc+eRH6/2upuXwa8PwqB2MSydcagZWazgK3uvoH2+xLrth+DSRHR7cdPJvrb806OjH7tJnoKD2AL0dFbT7ZkGWwuBR5x98eBx4EKjoz3C3r3f2rA3zeFQGzabp2xaADb0i1mlgVMdPdXgl1aX+bwvsS67ceg4e5PuPsLRE8HbQKe4gjoF9FTJS2bjZUAP6YHW7L0d6NjsI/3f+stI3o65Eh4v6D9z4cebaXTz+0GtGI4Zmb2H0A10Tfv1wPdnliZ2deIzsRoIvpb5ReAi2jVl+CH8jaiv5ks8fens91M9ILeH9397QFofqeCULua6MXTq4j2LaH7FVws/DbR6zh5wP3E0IdgquG1RCcB/LcPvimiBcAFRDeNHEt0Vsy3SdD3y8zOJ7odzpXu/nTbz4dY+zIY3jeFgIhIiOl0kIhIiCkERERCTCEgIhJiCgERkRBTCIh0g5mVDHQbROJJISASo2CaY9tV17E8LyWYBy4y6CgERGLk7pXAmh489RKiW0KLDDoKAZG+10R0gZTIoKNdRCXUzCwV+DKwluiS/5XAV4D7gDnAm0SX+3+d6ChgfPC8TOBLRPcsSnb3h4JN+k4juvXDx4nuMV9B9P9ZJFgNPBT4FdHtO1KI7i9zI3A0MA2oBfa6+3N93HURQCMBkS8AL7r7M8Bmort2VhDd8OsW4GngSuAxd/8tsCF43peBpzx6Z6nZZpYUHI8iulPmPwE7zGxU8FqrgGODU0pLgN8Q3WRsCdFthr/k7g+6+++AT5iZ9X3XRRQCIicQHQVA9IYgxxP9TXyVuze5ex3R+xZsaPO8KcB4M5sDbCS6KyTAUnc/GPxx4BSie8Yf4v0NxVr2lG/ZDM2A7FavfZDoLqkifU4hIGG3AhgdHI8Ovm5rCzAsOB4a/L0e2BDsZPqou1d38Prp7v6/wP8QvbVgR1o/P43oltIifU4hIGH3c+CjZvZRYBzRU0HTzOyjwfUCiH6Af8nMLgSyzWw8MA+4zMwuA04ECF5jmpmdEnxdAsw1szSiH+wnmtkY4FiitxIcF/wZCzxkZp8xs08Afx6kWybLEUi7iIqIhJhGAiIiIaYQEBEJMYWAiEiIKQREREJMISAiEmIKARGREFMIiIiE2P8BQpVQdi+x6Q0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_dup_a['orderAmo'],label='用户消费金额的分布图')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba430b86d8>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba430b86d8>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '消费金额累计百分比')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba54045e10>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba54045e10>"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '消费商品数累计百分比')"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 用户累计消费金额占比\n",
    "\n",
    "#按ID分组,并求和,再对订单金额进行排序(默认从小到大),最后通过匿名函数对每一行进行累计求和占比\n",
    "user_cumsum=df_dup[['orderAmo','用户购买商品总数量']].sort_values('orderAmo').apply(lambda x: x.cumsum()/x.sum())\n",
    "plt.subplot(211)\n",
    "user_cumsum.reset_index().orderAmo.plot(figsize=(8,8))\n",
    "plt.title('消费金额累计百分比')\n",
    "plt.subplot(212)\n",
    "user_cumsum.reset_index()['用户购买商品总数量'].plot()\n",
    "plt.title('消费商品数累计百分比')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 用户消费行为分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 第一次购买时间分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba502d8e10>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 计算第一次购买时间分布\n",
    "df['month'].groupby(df['userMobile']).min().value_counts().plot(figsize=(12,5))\n",
    "#等同于grouped_user.min().order_dt.value_counts().plot(figsize=(12,5))\n",
    "# 6月发生较大下跌  渠道发生变化，或者其他  可以做一些假设"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 最后一次购买时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba56596940>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### 最后一次购买时间\n",
    "df['month'].groupby(df['userMobile']).max().value_counts().plot(figsize=(12,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "大部分最后一次购买在前三个月，说明很多用户购买一次后就不再进行购买\n",
    "随着时间递增，最后一次购买数在递增，消费呈线性流失上升的情况"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 新老客户消费比"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### a.多少用户只消费一次"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userMobile</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>orderCode</th>\n",
       "      <th>orderAmo</th>\n",
       "      <th>instalmentRate</th>\n",
       "      <th>idNum</th>\n",
       "      <th>month</th>\n",
       "      <th>用户购买商品总数量</th>\n",
       "      <th>用户购买商品总金额</th>\n",
       "      <th>用户下单次数</th>\n",
       "      <th>用户购买商品平均价格</th>\n",
       "      <th>用户性别</th>\n",
       "      <th>用户最后一次购买时间距现在天数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13001031231</td>\n",
       "      <td>2016-09-18</td>\n",
       "      <td>311609181545532384917</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>362401198510210015</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>男</td>\n",
       "      <td>1157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13001140007</td>\n",
       "      <td>2016-07-25</td>\n",
       "      <td>311607251723422383739</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>14032119920418421X</td>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>男</td>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13001454260</td>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>3116082719125323816573</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>130582199103132033</td>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>男</td>\n",
       "      <td>1179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13001517860</td>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>3116082719474423818936</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>370304198906042250</td>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>男</td>\n",
       "      <td>1179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>13001645455</td>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>3116082722283123830172</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>230122199602101018</td>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>男</td>\n",
       "      <td>1179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     userMobile  orderTime               orderCode  orderAmo  instalmentRate  \\\n",
       "0   13001031231 2016-09-18   311609181545532384917      69.0            0.03   \n",
       "2   13001140007 2016-07-25   311607251723422383739    4485.0            0.12   \n",
       "4   13001454260 2016-08-27  3116082719125323816573      69.0            0.03   \n",
       "5   13001517860 2016-08-27  3116082719474423818936      69.0            0.03   \n",
       "10  13001645455 2016-08-27  3116082722283123830172      69.0            0.03   \n",
       "\n",
       "                 idNum      month  用户购买商品总数量  用户购买商品总金额  用户下单次数  用户购买商品平均价格  \\\n",
       "0   362401198510210015 2016-09-01          1       69.0       1        69.0   \n",
       "2   14032119920418421X 2016-07-01          1     4485.0       1      4485.0   \n",
       "4   130582199103132033 2016-08-01          1       69.0       1        69.0   \n",
       "5   370304198906042250 2016-08-01          1       69.0       1        69.0   \n",
       "10  230122199602101018 2016-08-01          1       69.0       1        69.0   \n",
       "\n",
       "   用户性别  用户最后一次购买时间距现在天数  \n",
       "0     男             1157  \n",
       "2     男             1212  \n",
       "4     男             1179  \n",
       "5     男             1179  \n",
       "10    男             1179  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dup.query('用户下单次数==1').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4317511058767447"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算只消费了一次的用户人数(结果为:61588)\n",
    "one_consumed=df_dup.query('用户下单次数==1').shape[0]\n",
    "# 计算总的消费人数(结果为:142647)\n",
    "all_consumed=df_dup.shape[0]\n",
    "#计算多少用户只消费一次\n",
    "one_consumed/all_consumed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可见，对于该产品，将近一半的用户值消费一次就流失掉了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "#### b.计算每月新客占比并作出其百分比折线图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba3a0e4fd0>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 按月份和用户ID分组\n",
    "grouped_month_user=df_dup.groupby('month')['userMobile'].count().reset_index().rename(columns={'userMobile':'total'})\n",
    "grouped_month_one_user=df_dup[df_dup['用户下单次数'] == 1].groupby('month')['userMobile'].count().reset_index().rename(columns={'userMobile':'one_user'})\n",
    "# grouped_month_one_user\n",
    "temp_group3 = pd.merge(grouped_month_user,grouped_month_one_user,how='left',on='month')\n",
    "temp_group3['新客占比'] = temp_group3['one_user'] /temp_group3['total']\n",
    "sns.lineplot(x='month',y='新客占比',data=temp_group3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "记录初始，新客居多，随着复购用户到来，逐步下降"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 用户分层"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RFM模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_products</th>\n",
       "      <th>order_amount</th>\n",
       "      <th>order_dt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userMobile</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13001031231</th>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2016-09-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001101801</th>\n",
       "      <td>18</td>\n",
       "      <td>82581.0</td>\n",
       "      <td>2017-02-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001140007</th>\n",
       "      <td>1</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>2016-07-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001352283</th>\n",
       "      <td>8</td>\n",
       "      <td>31677.0</td>\n",
       "      <td>2016-11-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001454260</th>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2016-08-27</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             order_products  order_amount   order_dt\n",
       "userMobile                                          \n",
       "13001031231               1          69.0 2016-09-18\n",
       "13001101801              18       82581.0 2017-02-15\n",
       "13001140007               1        4485.0 2016-07-25\n",
       "13001352283               8       31677.0 2016-11-17\n",
       "13001454260               1          69.0 2016-08-27"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  RFM  \n",
    "rfm = df.pivot_table(index = 'userMobile',\n",
    "                     values = ['article.0.artCount','orderAmo','orderTime'],\n",
    "                     aggfunc = {'orderTime':'max',\n",
    "                                'orderAmo':'sum',\n",
    "                                'article.0.artCount':'sum'})\n",
    "rfm.columns = ['order_products','order_amount','order_dt']\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 解释：\n",
    "    R：消费最后一次消费时间的度量，数值越小越好\n",
    "    F：消费的总商品数，数值越大越好\n",
    "    M：消费的总金额，数值越大越好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>order_dt</th>\n",
       "      <th>R</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userMobile</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13001031231</th>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2016-09-18</td>\n",
       "      <td>191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001101801</th>\n",
       "      <td>18</td>\n",
       "      <td>82581.0</td>\n",
       "      <td>2017-02-15</td>\n",
       "      <td>41.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001140007</th>\n",
       "      <td>1</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>2016-07-25</td>\n",
       "      <td>246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001352283</th>\n",
       "      <td>8</td>\n",
       "      <td>31677.0</td>\n",
       "      <td>2016-11-17</td>\n",
       "      <td>131.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001454260</th>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>213.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              F        M   order_dt      R\n",
       "userMobile                                \n",
       "13001031231   1     69.0 2016-09-18  191.0\n",
       "13001101801  18  82581.0 2017-02-15   41.0\n",
       "13001140007   1   4485.0 2016-07-25  246.0\n",
       "13001352283   8  31677.0 2016-11-17  131.0\n",
       "13001454260   1     69.0 2016-08-27  213.0"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# R\n",
    "# 计算每位用户最后一次消费时间与全部用户最后一次消费时间的差值\n",
    "\n",
    "rfm['R']=-(rfm.order_dt-rfm.order_dt.max())/np.timedelta64(1,'D')\n",
    "rfm.rename(columns={'order_products':'F','order_amount':'M'},inplace=True)\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R</th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userMobile</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13001031231</th>\n",
       "      <td>-9.753132</td>\n",
       "      <td>-1.996803</td>\n",
       "      <td>-8391.212918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001101801</th>\n",
       "      <td>-159.753132</td>\n",
       "      <td>15.003197</td>\n",
       "      <td>74120.787082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001140007</th>\n",
       "      <td>45.246868</td>\n",
       "      <td>-1.996803</td>\n",
       "      <td>-3975.212918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001352283</th>\n",
       "      <td>-69.753132</td>\n",
       "      <td>5.003197</td>\n",
       "      <td>23216.787082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001454260</th>\n",
       "      <td>12.246868</td>\n",
       "      <td>-1.996803</td>\n",
       "      <td>-8391.212918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      R          F             M\n",
       "userMobile                                      \n",
       "13001031231   -9.753132  -1.996803  -8391.212918\n",
       "13001101801 -159.753132  15.003197  74120.787082\n",
       "13001140007   45.246868  -1.996803  -3975.212918\n",
       "13001352283  -69.753132   5.003197  23216.787082\n",
       "13001454260   12.246868  -1.996803  -8391.212918"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del rfm['order_dt']\n",
    "#应用匿名函数,判断每一行值与平均值大小关系\n",
    "rfm[['R','F','M']].apply(lambda x:x-x.mean()).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 客户层次的定义,RFM得分可根据业务定义打分,也可以通过K-means聚类模型,得出不同相似程度的数据集,并且根据每一个数据集的特点进行客户定义\n",
    "# 111，R>0,是距离平均消费时间要久，R越大 说明没有消费时间越久  ，F >0 M>0,消费次数和金额也是较高的，重要价值客户，依次类推\n",
    "# x - x.mean() （具体真实情况可以修改，不一定需要用均值）   切比雪夫也可以 > 200 极值人工处理掉\n",
    "def rfm_func(x):\n",
    "    level = x.apply(lambda x:'1' if x>= 0 else '0')\\\n",
    "     # 字符串拼接\n",
    "    label = level.R + level.F + level.M\n",
    "    d = {\n",
    "        '111':'重要价值客户', \n",
    "        '011':'重要保持客户',\n",
    "        '101':'重要挽留客户',\n",
    "        '001':'重要发展客户',\n",
    "        '110':'一般价值客户',\n",
    "        '010':'一般保持客户',\n",
    "        '100':'一般挽留客户',\n",
    "        '000':'一般发展客户'\n",
    "    }\n",
    "    result = d[label]\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>R</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userMobile</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13001031231</th>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>一般发展客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001101801</th>\n",
       "      <td>18</td>\n",
       "      <td>82581.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>重要保持客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001140007</th>\n",
       "      <td>1</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>一般挽留客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001352283</th>\n",
       "      <td>8</td>\n",
       "      <td>31677.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>重要保持客户</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001454260</th>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>一般挽留客户</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              F        M      R   label\n",
       "userMobile                             \n",
       "13001031231   1     69.0  191.0  一般发展客户\n",
       "13001101801  18  82581.0   41.0  重要保持客户\n",
       "13001140007   1   4485.0  246.0  一般挽留客户\n",
       "13001352283   8  31677.0  131.0  重要保持客户\n",
       "13001454260   1     69.0  213.0  一般挽留客户"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfm['label'] = rfm[['R','F','M']].apply(lambda x:x-x.mean()).apply(rfm_func,axis=1)\n",
    "rfm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>R</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一般价值客户</th>\n",
       "      <td>16486</td>\n",
       "      <td>2.359209e+07</td>\n",
       "      <td>1099808.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一般保持客户</th>\n",
       "      <td>36880</td>\n",
       "      <td>3.396609e+07</td>\n",
       "      <td>1267037.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一般发展客户</th>\n",
       "      <td>39969</td>\n",
       "      <td>6.603191e+07</td>\n",
       "      <td>5054060.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一般挽留客户</th>\n",
       "      <td>64963</td>\n",
       "      <td>1.712709e+08</td>\n",
       "      <td>12616969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重要价值客户</th>\n",
       "      <td>64105</td>\n",
       "      <td>2.236017e+08</td>\n",
       "      <td>3261170.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重要保持客户</th>\n",
       "      <td>188133</td>\n",
       "      <td>6.018738e+08</td>\n",
       "      <td>3429872.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重要发展客户</th>\n",
       "      <td>4681</td>\n",
       "      <td>2.509173e+07</td>\n",
       "      <td>384321.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重要挽留客户</th>\n",
       "      <td>12268</td>\n",
       "      <td>6.139570e+07</td>\n",
       "      <td>1523595.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             F             M           R\n",
       "label                                   \n",
       "一般价值客户   16486  2.359209e+07   1099808.0\n",
       "一般保持客户   36880  3.396609e+07   1267037.0\n",
       "一般发展客户   39969  6.603191e+07   5054060.0\n",
       "一般挽留客户   64963  1.712709e+08  12616969.0\n",
       "重要价值客户   64105  2.236017e+08   3261170.0\n",
       "重要保持客户  188133  6.018738e+08   3429872.0\n",
       "重要发展客户    4681  2.509173e+07    384321.0\n",
       "重要挽留客户   12268  6.139570e+07   1523595.0"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 计算每层客户R、F、M的和\n",
    "rfm.groupby('label').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "分析：可以看出，重要保持客户对于消费总金额的占比远大于其他客户的占比，这说明绝大部分收益是由重要保持客户贡献的，只要能保证这部分客户不流失和增加，那么公司收益将得到有力保障"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F</th>\n",
       "      <th>M</th>\n",
       "      <th>R</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>一般价值客户</th>\n",
       "      <td>4676</td>\n",
       "      <td>4676</td>\n",
       "      <td>4676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一般保持客户</th>\n",
       "      <td>8473</td>\n",
       "      <td>8473</td>\n",
       "      <td>8473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一般发展客户</th>\n",
       "      <td>30569</td>\n",
       "      <td>30569</td>\n",
       "      <td>30569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>一般挽留客户</th>\n",
       "      <td>52509</td>\n",
       "      <td>52509</td>\n",
       "      <td>52509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重要价值客户</th>\n",
       "      <td>13667</td>\n",
       "      <td>13667</td>\n",
       "      <td>13667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重要保持客户</th>\n",
       "      <td>24277</td>\n",
       "      <td>24277</td>\n",
       "      <td>24277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重要发展客户</th>\n",
       "      <td>2341</td>\n",
       "      <td>2341</td>\n",
       "      <td>2341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重要挽留客户</th>\n",
       "      <td>6135</td>\n",
       "      <td>6135</td>\n",
       "      <td>6135</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            F      M      R\n",
       "label                      \n",
       "一般价值客户   4676   4676   4676\n",
       "一般保持客户   8473   8473   8473\n",
       "一般发展客户  30569  30569  30569\n",
       "一般挽留客户  52509  52509  52509\n",
       "重要价值客户  13667  13667  13667\n",
       "重要保持客户  24277  24277  24277\n",
       "重要发展客户   2341   2341   2341\n",
       "重要挽留客户   6135   6135   6135"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 人数\n",
    "rfm.groupby('label').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 各类类型用户占比\n",
    "use_c = rfm.groupby('label').count()\n",
    "plt.axis('equal')\n",
    "labels = ['一般价值客户','一般保持客户','一般发展客户','一般挽留客户','重要价值客户','重要保持客户','重要发展客户','重要挽留客户']\n",
    "plt.pie(use_c['M'],\n",
    "       autopct='%3.1f%%',\n",
    "        labels = labels,\n",
    "        pctdistance=0.9,\n",
    "       labeldistance = 1.2,\n",
    "       radius=3,\n",
    "       startangle = 15);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 分析：\n",
    "    1:可以看到一般挽留用户占比36.8%，占比巨大，这是用户运营中的巨大隐患，需要引起重视\n",
    "    2:占比第二大的是一般发展用户，21.4%，需要对这部分用户注重引导转化，提高用户价值\n",
    "    \n",
    "##### 注意:\n",
    "    1,本处用均值划分数据，很可能带来极值的影响，所以可以考虑用例如中位数在做一版"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用户状态分层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>month</th>\n",
       "      <th>2016-06-01 00:00:00</th>\n",
       "      <th>2016-07-01 00:00:00</th>\n",
       "      <th>2016-08-01 00:00:00</th>\n",
       "      <th>2016-09-01 00:00:00</th>\n",
       "      <th>2016-10-01 00:00:00</th>\n",
       "      <th>2016-11-01 00:00:00</th>\n",
       "      <th>2016-12-01 00:00:00</th>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <th>2017-02-01 00:00:00</th>\n",
       "      <th>2017-03-01 00:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userMobile</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13001031231</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001101801</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001140007</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001352283</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001454260</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "month        2016-06-01  2016-07-01  2016-08-01  2016-09-01  2016-10-01  \\\n",
       "userMobile                                                                \n",
       "13001031231         0.0         0.0         0.0         1.0         0.0   \n",
       "13001101801         0.0         1.0         4.0         3.0         3.0   \n",
       "13001140007         0.0         1.0         0.0         0.0         0.0   \n",
       "13001352283         0.0         3.0         0.0         2.0         2.0   \n",
       "13001454260         0.0         0.0         1.0         0.0         0.0   \n",
       "\n",
       "month        2016-11-01  2016-12-01  2017-01-01  2017-02-01  2017-03-01  \n",
       "userMobile                                                               \n",
       "13001031231         0.0         0.0         0.0         0.0         0.0  \n",
       "13001101801         1.0         3.0         0.0         3.0         0.0  \n",
       "13001140007         0.0         0.0         0.0         0.0         0.0  \n",
       "13001352283         1.0         0.0         0.0         0.0         0.0  \n",
       "13001454260         0.0         0.0         0.0         0.0         0.0  "
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pivoted_counts = df.pivot_table(index = 'userMobile',\n",
    "                                  columns = 'month',\n",
    "                                  values = 'orderTime',\n",
    "                                  aggfunc = 'count').fillna(0)\n",
    "# pivoted_counts.head()\n",
    "pivoted_counts.head()\n",
    "# 按月份进行对比，1月份哪些是购买的，再去对比二月份哪些是购买的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 判断用户消费状态 消费过的为1 ，没消费过的为0\n",
    "data_purchase = pivoted_counts.applymap(lambda x: 1 if x > 0 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [],
   "source": [
    "#用户状态\n",
    "def active_status(data):\n",
    "    \"\"\"\n",
    "    1 若本月没有消费\n",
    "        1.1若之前是未注册，则依旧为未注册    unreg\n",
    "        1.2若之前有消费，则为流失/不活跃     unactive\n",
    "        1.3其他情况，为未注册               unreg\n",
    "    2 若本月有消费\n",
    "        2.1若是第一次消费，则为新用户        new\n",
    "        2.2若之前有过消费，则上个月为不活跃，则为回流    return\n",
    "        2.3若上个月为未注册，则为新用户      new\n",
    "    除此之外，为活跃                       active\n",
    "    \n",
    "    \"\"\"\n",
    "    status = []\n",
    "    num = data.shape[0]\n",
    "    for i in range(num):\n",
    "        #若本月没有消费\n",
    "#         print(data[i])\n",
    "        if data[i] == 0:\n",
    "            if len(status) > 0:\n",
    "                if status[i-1] == 'unreg':\n",
    "                    status.append('unreg')\n",
    "                else:\n",
    "                    status.append('unactive')\n",
    "            else:\n",
    "                status.append('unreg')                  \n",
    "        #若本月消费\n",
    "        else:\n",
    "            if len(status) == 0:\n",
    "                status.append('new')\n",
    "            else:\n",
    "                if status[i-1] == 'unactive':\n",
    "                    status.append('return')\n",
    "                elif status[i-1] == 'unreg':\n",
    "                    status.append('new')\n",
    "                else:\n",
    "                    status.append('active')\n",
    "    # 这里需要对返回的值进行转换，将列表转为Series\n",
    "    return pd.Series(status, index = pivoted_counts.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [],
   "source": [
    "purchase_stats = data_purchase.apply(active_status,axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "未注册不希望参与处理 设置为空值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>month</th>\n",
       "      <th>2016-06-01 00:00:00</th>\n",
       "      <th>2016-07-01 00:00:00</th>\n",
       "      <th>2016-08-01 00:00:00</th>\n",
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       "      <td>242.0</td>\n",
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       "      <td>6094</td>\n",
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       "      <td>562</td>\n",
       "    </tr>\n",
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       "      <th>new</th>\n",
       "      <td>370.0</td>\n",
       "      <td>58171.0</td>\n",
       "      <td>34936.0</td>\n",
       "      <td>28949</td>\n",
       "      <td>11858</td>\n",
       "      <td>4494</td>\n",
       "      <td>2377</td>\n",
       "      <td>251</td>\n",
       "      <td>592</td>\n",
       "      <td>649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>return</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1845</td>\n",
       "      <td>3658</td>\n",
       "      <td>3439</td>\n",
       "      <td>2104</td>\n",
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       "      <td>1116</td>\n",
       "      <td>1411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unactive</th>\n",
       "      <td>NaN</td>\n",
       "      <td>128.0</td>\n",
       "      <td>46140.0</td>\n",
       "      <td>77332</td>\n",
       "      <td>107714</td>\n",
       "      <td>124751</td>\n",
       "      <td>133931</td>\n",
       "      <td>140610</td>\n",
       "      <td>140048</td>\n",
       "      <td>140025</td>\n",
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       "month     2016-06-01  2016-07-01  2016-08-01  2016-09-01  2016-10-01  \\\n",
       "active           NaN       242.0     12401.0       14300       11054   \n",
       "new            370.0     58171.0     34936.0       28949       11858   \n",
       "return           NaN         NaN         NaN        1845        3658   \n",
       "unactive         NaN       128.0     46140.0       77332      107714   \n",
       "\n",
       "month     2016-11-01  2016-12-01  2017-01-01  2017-02-01  2017-03-01  \n",
       "active          6094        2743         247         242         562  \n",
       "new             4494        2377         251         592         649  \n",
       "return          3439        2104         298        1116        1411  \n",
       "unactive      124751      133931      140610      140048      140025  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    },
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       "      <th></th>\n",
       "      <th>active</th>\n",
       "      <th>new</th>\n",
       "      <th>return</th>\n",
       "      <th>unactive</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month</th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <td>370.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>242.0</td>\n",
       "      <td>58171.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>128.0</td>\n",
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       "      <th>2016-08-01</th>\n",
       "      <td>12401.0</td>\n",
       "      <td>34936.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46140.0</td>\n",
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       "      <td>14300.0</td>\n",
       "      <td>28949.0</td>\n",
       "      <td>1845.0</td>\n",
       "      <td>77332.0</td>\n",
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       "      <th>2016-10-01</th>\n",
       "      <td>11054.0</td>\n",
       "      <td>11858.0</td>\n",
       "      <td>3658.0</td>\n",
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      ],
      "text/plain": [
       "             active      new  return  unactive\n",
       "month                                         \n",
       "2016-06-01      0.0    370.0     0.0       0.0\n",
       "2016-07-01    242.0  58171.0     0.0     128.0\n",
       "2016-08-01  12401.0  34936.0     0.0   46140.0\n",
       "2016-09-01  14300.0  28949.0  1845.0   77332.0\n",
       "2016-10-01  11054.0  11858.0  3658.0  107714.0"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_status_ct = purchase_stats.replace('unreg',np.NaN).apply(lambda x : pd.value_counts(x))\n",
    "purchase_status_ct\n",
    "\n",
    "#将上面透视表转置\n",
    "purchase_status_ct.fillna(0).T.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba3a777320>"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '用户分层')"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#作用户状态分层面积图\n",
    "purchase_stats_ct=purchase_status_ct.fillna(0).T\n",
    "purchase_stats_ct.plot.area(figsize=(12,5))\n",
    "plt.title('用户分层')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "由上可知不同客户活跃状态,根据业务需求采取不同的运营策略,如拉新,引流,促活,召回等"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>userMobile</th>\n",
       "      <th>orderTime</th>\n",
       "      <th>orderCode</th>\n",
       "      <th>orderAmo</th>\n",
       "      <th>instalmentRate</th>\n",
       "      <th>idNum</th>\n",
       "      <th>month</th>\n",
       "      <th>用户购买商品总数量</th>\n",
       "      <th>用户购买商品总金额</th>\n",
       "      <th>用户下单次数</th>\n",
       "      <th>用户购买商品平均价格</th>\n",
       "      <th>用户性别</th>\n",
       "      <th>用户最后一次购买时间距现在天数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13001031231</td>\n",
       "      <td>2016-09-18</td>\n",
       "      <td>311609181545532384917</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>362401198510210015</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>男</td>\n",
       "      <td>1157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13001101801</td>\n",
       "      <td>2017-02-15</td>\n",
       "      <td>3117021500120000010</td>\n",
       "      <td>5199.0</td>\n",
       "      <td>0.144</td>\n",
       "      <td>220722199212264218</td>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>18</td>\n",
       "      <td>82581.0</td>\n",
       "      <td>18</td>\n",
       "      <td>4587.833333</td>\n",
       "      <td>男</td>\n",
       "      <td>1007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13001140007</td>\n",
       "      <td>2016-07-25</td>\n",
       "      <td>311607251723422383739</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>0.120</td>\n",
       "      <td>14032119920418421X</td>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4485.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4485.000000</td>\n",
       "      <td>男</td>\n",
       "      <td>1212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13001352283</td>\n",
       "      <td>2016-11-17</td>\n",
       "      <td>311611170231442380142</td>\n",
       "      <td>2800.0</td>\n",
       "      <td>0.144</td>\n",
       "      <td>372527198010293716</td>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>8</td>\n",
       "      <td>31677.0</td>\n",
       "      <td>8</td>\n",
       "      <td>3959.625000</td>\n",
       "      <td>男</td>\n",
       "      <td>1097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13001454260</td>\n",
       "      <td>2016-08-27</td>\n",
       "      <td>3116082719125323816573</td>\n",
       "      <td>69.0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>130582199103132033</td>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>1</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>男</td>\n",
       "      <td>1179</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    userMobile  orderTime               orderCode  orderAmo  instalmentRate  \\\n",
       "0  13001031231 2016-09-18   311609181545532384917      69.0           0.030   \n",
       "1  13001101801 2017-02-15     3117021500120000010    5199.0           0.144   \n",
       "2  13001140007 2016-07-25   311607251723422383739    4485.0           0.120   \n",
       "3  13001352283 2016-11-17   311611170231442380142    2800.0           0.144   \n",
       "4  13001454260 2016-08-27  3116082719125323816573      69.0           0.030   \n",
       "\n",
       "                idNum      month  用户购买商品总数量  用户购买商品总金额  用户下单次数   用户购买商品平均价格  \\\n",
       "0  362401198510210015 2016-09-01          1       69.0       1    69.000000   \n",
       "1  220722199212264218 2017-02-01         18    82581.0      18  4587.833333   \n",
       "2  14032119920418421X 2016-07-01          1     4485.0       1  4485.000000   \n",
       "3  372527198010293716 2016-11-01          8    31677.0       8  3959.625000   \n",
       "4  130582199103132033 2016-08-01          1       69.0       1    69.000000   \n",
       "\n",
       "  用户性别  用户最后一次购买时间距现在天数  \n",
       "0    男             1157  \n",
       "1    男             1007  \n",
       "2    男             1212  \n",
       "3    男             1097  \n",
       "4    男             1179  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dup.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用户购买周期(按订单)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "userMobile         \n",
       "13001031231  263754       NaT\n",
       "13001101801  101462       NaT\n",
       "             118916    5 days\n",
       "             121688    1 days\n",
       "             145442   10 days\n",
       "             198716   15 days\n",
       "             256749   19 days\n",
       "             261646    1 days\n",
       "             282202    6 days\n",
       "             322197   15 days\n",
       "Name: orderTime, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#计算用户相邻订单日期的差值,其中shift()函数是指将数据进行移动,默认axis=0\n",
    "order_diff=df.groupby('userMobile').apply(lambda x: x.orderTime-x.orderTime.shift())\n",
    "order_diff.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba3a19e048>"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '用户消费周期分布')"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# order_diff.reset_index().rename(columns={})\n",
    "\n",
    "#作用户消费周期分布：\n",
    "(order_diff/np.timedelta64(1,'D')).hist(bins=20)\n",
    "plt.title('用户消费周期分布')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba3c321ac8>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '用户生命周期分布')"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#usertime_diff代表用户消费首次订单与最后一次订单间隔时间\n",
    "usertime_diff=df['orderTime'].groupby(df['userMobile']).max()-df['orderTime'].groupby(df['userMobile']).min() ### 最大值 - 最小值\n",
    "usertime_diff=usertime_diff.reset_index()\n",
    "usertime_diff=usertime_diff['orderTime']/np.timedelta64(1,'D')\n",
    "usertime_diff[usertime_diff>0].hist(bins=40)\n",
    "plt.title('用户生命周期分布')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绝大部分用户用完即走"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 复购率和回购率分析\n",
    "    复购率:自然月内，购买多次的用户占比\n",
    "    回购率:曾今购买过的用户在某一时期内的再次购买占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "复购率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>month</th>\n",
       "      <th>2016-06-01 00:00:00</th>\n",
       "      <th>2016-07-01 00:00:00</th>\n",
       "      <th>2016-08-01 00:00:00</th>\n",
       "      <th>2016-09-01 00:00:00</th>\n",
       "      <th>2016-10-01 00:00:00</th>\n",
       "      <th>2016-11-01 00:00:00</th>\n",
       "      <th>2016-12-01 00:00:00</th>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <th>2017-02-01 00:00:00</th>\n",
       "      <th>2017-03-01 00:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userMobile</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13001031231</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001101801</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001140007</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001352283</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001454260</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "month        2016-06-01  2016-07-01  2016-08-01  2016-09-01  2016-10-01  \\\n",
       "userMobile                                                                \n",
       "13001031231         0.0         0.0         0.0         1.0         0.0   \n",
       "13001101801         0.0         1.0         4.0         3.0         3.0   \n",
       "13001140007         0.0         1.0         0.0         0.0         0.0   \n",
       "13001352283         0.0         3.0         0.0         2.0         2.0   \n",
       "13001454260         0.0         0.0         1.0         0.0         0.0   \n",
       "\n",
       "month        2016-11-01  2016-12-01  2017-01-01  2017-02-01  2017-03-01  \n",
       "userMobile                                                               \n",
       "13001031231         0.0         0.0         0.0         0.0         0.0  \n",
       "13001101801         1.0         3.0         0.0         3.0         0.0  \n",
       "13001140007         0.0         0.0         0.0         0.0         0.0  \n",
       "13001352283         1.0         0.0         0.0         0.0         0.0  \n",
       "13001454260         0.0         0.0         0.0         0.0         0.0  "
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 作透视表，计算客户每个月的消费次数\n",
    "pivoted_counts=df.pivot_table(index='userMobile',\n",
    "                              columns='month',\n",
    "                              values='orderTime',\n",
    "                              aggfunc='count').fillna(0)\n",
    "pivoted_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>month</th>\n",
       "      <th>2016-06-01 00:00:00</th>\n",
       "      <th>2016-07-01 00:00:00</th>\n",
       "      <th>2016-08-01 00:00:00</th>\n",
       "      <th>2016-09-01 00:00:00</th>\n",
       "      <th>2016-10-01 00:00:00</th>\n",
       "      <th>2016-11-01 00:00:00</th>\n",
       "      <th>2016-12-01 00:00:00</th>\n",
       "      <th>2017-01-01 00:00:00</th>\n",
       "      <th>2017-02-01 00:00:00</th>\n",
       "      <th>2017-03-01 00:00:00</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>userMobile</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13001031231</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001101801</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001140007</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001352283</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13001454260</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "month        2016-06-01  2016-07-01  2016-08-01  2016-09-01  2016-10-01  \\\n",
       "userMobile                                                                \n",
       "13001031231         NaN         NaN         NaN         0.0         NaN   \n",
       "13001101801         NaN         0.0         1.0         1.0         1.0   \n",
       "13001140007         NaN         0.0         NaN         NaN         NaN   \n",
       "13001352283         NaN         1.0         NaN         1.0         1.0   \n",
       "13001454260         NaN         NaN         0.0         NaN         NaN   \n",
       "\n",
       "month        2016-11-01  2016-12-01  2017-01-01  2017-02-01  2017-03-01  \n",
       "userMobile                                                               \n",
       "13001031231         NaN         NaN         NaN         NaN         NaN  \n",
       "13001101801         0.0         1.0         NaN         1.0         NaN  \n",
       "13001140007         NaN         NaN         NaN         NaN         NaN  \n",
       "13001352283         0.0         NaN         NaN         NaN         NaN  \n",
       "13001454260         NaN         NaN         NaN         NaN         NaN  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 购买大于1次的 赋值为1 ，然后小于等于1 的 如果是购买次数是0，则赋值为空，否则 就是购买一次，赋值为0\n",
    "purchase_r = pivoted_counts.applymap(lambda x: 1 if x > 1 else np.NaN if x == 0 else 0)\n",
    "purchase_r.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ba04ed9908>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '复购率')"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#计算复购率\n",
    "(purchase_r.sum()/purchase_r.count()).plot(figsize=(10,4))\n",
    "plt.title('复购率')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "2016-06-01    NaN\n",
       "2016-07-01    NaN\n",
       "2016-08-01    NaN\n",
       "2016-09-01    0.0\n",
       "2016-10-01    NaN\n",
       "2016-11-01    NaN\n",
       "2016-12-01    NaN\n",
       "2017-01-01    NaN\n",
       "2017-02-01    NaN\n",
       "2017-03-01    NaN\n",
       "Name: 13001031231, dtype: float64"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "purchase_r.iloc[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def purchase_back(data):\n",
    "#     # 定义一个函数，将消费两次以上记为1，消费一次记为0，没有消费记为空值：\n",
    "#     status = []\n",
    "#     print(len(data))\n",
    "#     for i in range(10):\n",
    "#         if data[i] == 1: # 本月进行过消费\n",
    "#             if data[i+1] == 1: # 下一月是否进行消费\n",
    "#                 status.append(1) #消费为1 回购了\n",
    "#             if data[i+1] == 0:\n",
    "#                 status.append(0) # 未消费则为0 没有回购\n",
    "#         else:\n",
    "#             status.append(np.NaN) # 之前没消费则不计\n",
    "#     status.append(np.NaN) # 最后一个月没有判断需要补上\n",
    "#     return pd.Series(status,data_purchase.columns)\n",
    "\n",
    "# #对透视表应用函数purchase_back:\n",
    "# # purchase_b = \n",
    "# data_purchase.iloc[:5,:].apply(purchase_back, axis =1)\n",
    "# # purchase_b.iloc[0,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
